Tag Archives: ecology

It’s been a long time since I’ve written in monkey’s uncle. Life has gotten pretty busy and my seeming inability to write brief entries has led me to neglect the blog this year. However, I am freshly back from the Ecology and Evolution of Infectious Disease Conference in Fort Collins, Colorado and feel compelled to give my annual run-down. The conference was hosted by friend and colleague Mike Antolin, Sue Vandewoude, and my erstwhile post-doc, now CSU researcher, Dan Salkeld. Nice job, folks, on a very successful conference.

EEID is pretty much the best meeting. As I noted in last year’s post, I love its future-orientation. EEID is a meeting that foregrounds the work of junior scientists and there was, as ever, a tremendous array of human capital on display at this meeting. This drives home to me the importance of investment in professional training and research programs that specifically develop human capital. This community exists in large measure because of the innovative program jointly offered by NSF and NIH. Thanks as ever to the vision and hard work of Josh Rosenthal, Sam Scheiner, and all the funders (e.g., support from The Gates Foundation can be found all around this conference) for this area. It’s always great to catch up with smart, fun friends. Plenty of time was spent talking science and drinking craft beer (what a beer town Ft. Collins is!) with the likes of Peter Hudson, Jessica Metcalf, Ottar Bjornstad, Aaron King, Mike Antolin, Tony Goldberg, Issa Cattadori, Maciej Boni, Marm Kilpatrick and, of course, Dan Salkeld. It was nice to meet and chat, if only briefly, with my sometime remote collaborator Paul Sharp, who gave what I understand to be an extremely stimulating keynote on the complicated and surprising evolution of malaria (alas, I missed it as I was delayed getting to Ft. Collins). I also spent some quality time learning about acquired immunity in dogs with Colin Parrish. This may come in handy for some ideas that Jess Metcalf and I have been playing around with.

There is a great tradition of the EEID hike and closing banquet/dance. Ft. Collins provided a beautiful and challenging hike out in Lory State Park. The view from the top of Arthur’s Peak was pretty amazing.

View from the top of the trail on Arthur’s Peak, Lory State Park, Ft. Collins.

At Wednesday’s banquet, I’m afraid to say that Princeton once again dominated the dance floor as we all rocked out to the amazing Denver funk/rock/jam band Kinetix (great choice, Mike). The Stanford showing was disappointing in part because of the early departure of some of our most enthusiastic dancers. Don’t get cocky though, Princeton. We’ll be gunning for you next year.

The entirety of Tuesday morning’s session was given over to communicating science. Dan Salkeld warmed up the crowd with some hilarious examples of the reporting frenzy that ensued following the publication of our paper on plague dynamics in prairie dog towns or, more recently, Hillary Young‘s work showing that excluding large ruminants increases rodent density in Kenya. Wow. Dan also used my Stanford colleagueRebecca Bird‘s work as an example of how an unexpected story can engage readers and listeners. My collaborator Tony Goldberg gave a talk that was also not lacking in ridiculous headlines thanks to his “viral” nose-tickwork. David Quammen, author of outstanding popular science books such as The Song of the Dodo and Spillover (which Bill Durham and I use for our class on environmental change and emerging infectious disease), gave a terrific presentation in which he consolidated a lot of nice, practical advice on the craft of writing engaging work into 18 points, amply illustrated by anecdotes of characters from our field. Sonia Altizer from the University of Georgia introduced the crowd to the opportunities (and pitfalls) of citizen science and suggested that it might just be possible to engage the public in disease ecology data collection. Some examples she identified included the granddaddy of citizen-science in the US run by the Laboratory of Ornithology at Cornell, the ZomBee Watch at SFSU, and her own Project MonarchHealth. If I had to summarize this session in one pithy phrase, I think it would have to be “Yay, ecologists!”

Quammen took to Twitter to call us out for being behind the curve with respect to social media.

@lfoquet I’ll ask. But I think #EEID2014 is a quietish conference, not high on podcasts or social media. Tell me, others, if I’m wrong.

While there were, in fact, a few of us tweeting the occasional tidbit from the conference, I think his general point is valid. This stuff is intrinsically interesting and we can do a much better job communicating to broad publics.

Some talks that really caught my attention.

Ary Hoffmann gave a great talk about the complexities of using bacteria of the genus Wolbachia to control the Aedes mosquitoes that transmit dengue in Australia (and elsewhere). Wolbachia infects mosquitoes and can have a variety of effects on their biology. The reason artificial infection of mosquitoes wit this bacterium seems so promising as a means of biological control is that the offspring of crosses between infected and uninfected mosquitoes are not viable. This is obviously a very substantial fitness cost to the mosquitoes and this creates serious challenges for getting the infected mosquitoes to persist and take over local populations. Hoffmann presented a cool result about the invasibility of infected mosquitoes wherein in the early phases of introduction there is an unstable point in the mosquito dynamics. At this point, if the infected mosquitoes are above a threshold, they will successfully invade, otherwise, they will die out because of the inherent fitness costs of the Wolbachia infection. One policy challenge that arises is that to get a local population of mosquitoes above the invasibility threshold, researchers and vector-control specialists have to sometimes introduce a lot of mosquitoes. This means that the number of mosquitoes locally can increase substantially and, as you can imagine, this isn’t always popular with communities.

Fellow Anthropologist Aaron Blackwell from UCSB gave a fantastic talk on our “old friends”, the helminths (cue the freaky electron micrograph of a helminth’s mouth!). Aaron participates in the Tsimane Health and Life History Project which was started by colleagues Mike Gurven (also at UCSB) and Hilly Kaplan (New Mexico). Using sophisticated multi-state Markov hazard models (go Anthropology!), Aaron showed that co-infection with helminths and Giardia is less frequent than expected among this population that experiences ubiquitous exposure to both pathogens and that, in fact, infection with the one appears to be protective against infection with the other. One of the most provocative results he presented showed that helminth infection actually lowered systolic blood pressure in men by an amount equivalent to the increase that comes from aging ten years. Chronic helminthic infection may be a reason why Tsimane men’s systolic blood pressure does not rise precipitously with age as it does in the US. This result, which may provide fresh insights into the mechanisms of hypertension, a major source of morbidity in the US, struck me as particularly poignant given the demeaning comments made about NSF funding for work among the Tsimane from none other than Lamar Smith (R–TX), the chair of the House Committee on Science, Space, and Technology.

Anna Savage, a post-doc with the National Zoo in Washington DC, gave an awesome talk on the comparative immunogenetics of of frogs with respect to infection with the devastating fungal infection, chytridiomycosis. Chytridiomycosis has been identified as a major cause of amphibian extinction worldwide and Anna showed surprising heterogeneity in immune response across frog species. This is a subject with which I have only passing familiarity, but her talk demonstrated an amazing sophistication in integrating different levels of biological organization and making sense of a dauntingly complex problem. I would wager that Dr. Savage is one to keep an eye on.

The organizers tried a scheduling format that was a bit different from last year, wherein each session started with two half-hour talks generally given by somewhat more senior people. The second half of each session was then given over to brief ten-minute talks, typically delivered by more junior people. This format is nicely in keeping with the great EEID tradition of promoting the research of junior scientists. A few short talks that I found especially interesting included one by Sarah Hamer, from Texas A&M, on Chagas disease in the United States. She presented sobering data from national blood-bank surveillance showing a surprising number of Chagas-infected samples coming from donors with no history of travel to Latin America. When pushed by a questioner, she suggested that she would consider Chagas to be endemic in the US, at least in dogs and possibly even in people. Carrie Cizauskas, formerly of Wayne Getz‘s shop at Berkeley and now with Andy Dobson and Andrea Graham at Princeton, give a nice talk on the role of both stress and sex hormones in mediating macroparasite infection in wild ungulates in Etosha National Park, Namibia. Romain Garnier from Princeton described a very nifty image-processing approach to scanning large volumes of histological slides for indications of infection.

I perhaps didn’t see as many posters as I should have. The problem with the poster sessions is that one keeps running into various people one wants to talk to. I did manage to check out the poster of my former freshman advisee and current Princeton EEB student Cara Brook. She’s got an awesome PhD project studying the multi-host ecology of infectious disease in Malagasy fruit bats.

I’m looking forward to next year’s meeting at the University of Georgia already. I’m also looking forward to resuscitating the pedagogical workshop that used to be a signature feature of this EEID meeting. More on that later…

I am recently back from the Ecology and Evolution of Infectious Disease (EEID) Principal Investigators’ Meeting hosted by the Odum School of Ecology at the University of Georgia in lovely Athens. This is a remarable event, and a remarkable field, and I can’t remember ever being so energized after returning from a professional conference (which often leave me dismayed or even depressed about my field). EEID is an innovative, highly interdisciplinary funding program jointly managed by the National Science Foundation and the National Institutes of Health. I have been lucky enough to be involved with this program for the last six years. I’ve served on the scientific review panel a couple times and am now a Co-PI on twoprojects.

We had a big turn-out for our Uganda team in Athens and team members presented no fewer than four posters. The Stanford social networks/human dimensions team (including Laura Bloomfield, Shannon Randolph and Lucie Clech) presented a poster (“Multiplex Social Relations and Retroviral Transmission Risk in Rural Western Uganda”) on our preliminary analysis of the social network data. Simon Frost’s student at Cambridge, James Lester, presented a poster (“Networks, Disease, and the Kibale Forest”) analyzing our syndromic surveillance data. Sarah Paige from Wisconsin presented a poster on the socio-economic predictors of high-risk animal contact (“Beyond Bushmeat: Animal contact, injury, and zoonotic disease risk in western Uganda”) and Maria Ruiz-López, who works with Nelson Ting at Oregon, presented a poster on their work on developing the resources to do some serious population genetics on the Kibale red colobus monkeys (“Use of RNA-seq and nextRAD for the development of red colobus monkey genomic resource”).

Parviez Hosseini, from the EcoHealth Alliance, also presented a poster for our joint work on comparative spillover dynamics of avian influenza (“Comparative Spillover Dynamics of Avian Influenza in Endemic Countries”). I’m excited to get more work done on this project which is possible now that new post-doc Ashley Hazel has arrived from Michigan. Ashley will oversee the collection of relational data in Bangladesh and help us get this project into high gear.

The EEID conference has a unique take on poster presentations which make it much more enjoyable than the typical professional meeting. In general, I hate poster sessions. Now, don’t get me wrong: I see lots of scientific value in them and they can be a great way for people to have extended conversations about their work. They can be an especially great forum for students to showcase their work and start the long process of forming professional networking. However, there is an awkwardness to poster sessions that can be painful for the hapless conference attender who might want, say, to walk through the room in which a poster session is being held. These rooms tend to be heavy with the smell of desperation and one has to negotiate a gauntlet of suit-clad, doe-eyed graduate students desperate to talk to anyone who will listen about their work. “Please talk to me; I’m so lonely” is what I imagine them all saying as I briskly walk through, trying to look busy and purposeful (while keeping half an eye out for something really interesting!).

The scene at EEID is much different. All posters go up at the same time and the site-fidelity of poster presenters is the lowest I have ever seen. It has to be since, if everyone stuck by their poster, there wouldn’t be anyone to see any of them! What this did was allow far more mixing than I normally see at such sessions and avoid much of the inherent social awkwardness of a poster session. Posters also stayed up long past the official poster session. I continued to read posters for at least a day after the official session ended. Of course, it helps that there was all manner of great work being presented.

There were lots of great podium talks too. I was particularly impressed with the talks by Charlie King of Case Western on polyparasitism in Kenya, Maria Diuk-Wasser of Yale on the emergence of babesiosis in the Northeast, Jean Tsao (Michigan State) and Graham Hickling‘s (Tennessee) joint talk on Lyme disease in the Southeast, and Bethany Krebs’s talk on the role of robin social behavior in West Nile Virus outbreaks. Laura Pomeroy, from Ohio State, represented one of the other few teams with a substantial anthropological component extremely well, talking about the transmission dynamics of foot-and-mouth disease in Cameroon. Probably my favorite talk of the weekend was the last talk by Penn State’s Matt Thomas. They done awesome work elucidating the role of temperature variability on the transmission dynamics of malaria.

It turns out that this was the last EEID PI conference. Next year, the EEID PI conference will be combined with the other EEID conference which was originally organized at Penn State (and is there again this May). This combining of forces is, I’m sure, a good thing as it will reduce confusion and probably make it more likely that all the people I want to see have a better chance of showing up. I just hope that this new, larger conference retains the charms of the EEID PI conference.

EEID is a new, interdisciplinary field that has grown thanks to some disproportionately large contributions of a few, highly energetic people. One of the principals in this realm is definitely Sam Scheiner, the EEID program officer at NSF. The EEID PI meeting has basically been Sam’s baby for the past 10 years. Sam has done an amazing job creating a community of interdisciplinary scholars and I’m sure I speak for every researcher who has been heavily involved with EEID when I express my gratitude for all his efforts.

One of the most important ideas in disease ecology is a hypothesis known as the “dilution effect”. The basic idea behind the dilution effect hypothesis is that biodiversity — typically measured by species richness, or the number of different species present in a particular spatially defined locality — is protective against infection with zoonotic pathogens (i.e., pathogens transmitted to humans through animal reservoirs). The hypothesis emerged from analysis of Lyme disease ecology in the American Northeast by Richard Ostfeld and his colleagues and students from the Cary Institute for Ecosystem Studies in Millbrook, New York. Lyme disease ecology is incredibly complicated, and there are a couple different ways that the dilution effect can come into play even in this one disease system, but I will try to render it down to something easily digestible.

Lyme disease is caused by a spirochete bacterium Borrelia burgdorferi. It is a vector-borne disease transmitted by hard-bodied ticks of the genus >Ixodes. These ticks are what is known as hemimetabolous, meaning that they experience incomplete metamorphosis involving larval and nymphal stages. Rather than a pupa, these larvae and nymphs resemble little bitty adults. An Ixodes tick takes three blood meals in its lifetime: one as a larva, once as a nymph, once as an adult. At different life-cycle stages, the ticks have different preferences for hosts. Larval ticks generally favor the white-footed mouse (Peromyscus leucopus) for their blood meal and this is where the catch is. It turns out that white-footed mice are extremely efficient reservoirs for Lyme disease. In fact, an infected mouse has as much as a 90% chance of transmitting infection to a larva feeding on it. The larvae then molt into nymphs and overwinter on the forest floor. Then, in spring or early summer a year after they first hatch from eggs, nymphs seek vertebrate hosts. If an individual tick acquired infection as a larva, it can now transmit to its next host. Nymphs are less particular about their choice of host and are happy to feed on humans (or just about any other available vertebrate host).

This is where the dilution effect comes in. The basic idea is that if there are more potential hosts such as chipmunks, shrews, squirrels, or skunks, there are more chances that an infected nymph will take a blood meal on a person. Furthermore, most of these hosts are much less efficient at transmitting the Lyme spirochete than are white-footed mice. This lowers the prevalence of infection and makes it more likely that it will go extinct locally. It’s not difficult to imagine the dilution effect working at the larval stage blood-meal too: if there are more species present (and the larvae are not picky about their blood meal), the risk of initial infection is also diluted.

In the highly-fragmented landscape of northeastern temperate woodlands, when there is only one species in a forest fragment, it is quite likely that it will be a white-footed mouse. These mice are very adaptable generalists that occur in a wide range of habitats from pristine woodland to degraded forest. Therefore, species-poor habitats tend to have mice but no other species. The idea behind the dilution effect is that by adding different species to the baseline of a highly depauperate assemblage of simply white-footed mice, the prevalence of nymphal infection will decline and the risk for zoonotic infection of people will be reduced.

It is not an exaggeration to say that the dilution-effect hypothesis is one of the two or three most important ideas in disease ecology and much of the explosion of interest in disease ecology can be attributed in part to such ideas. The dilution effect is also a nice idea. Wouldn’t it be great if every dollar we invested in the conservation of biodiversity potentially paid a dividend in reduced disease risk? However, its importance to the field or the beauty of the idea do not guarantee that it is actually scientifically correct.

One major issue with the dilution effect hypothesis is its problem with scale, arguably the central question in ecology. Numerous studies have shown that pathogen diversity is positively related to overall biodiversity at larger spatial scales. For example, in an analysis of global risk of emerging infectious diseases, Kate Jones and her colleagues form the London Zoological Society showed that globally, mammalian biodiversity is positively associated with the odds of an emerging disease. Work by Pete Hudson and colleagues at the Center for Infectious Disease Dynamics at Penn State showed that healthy ecosystems may actually be richer in parasite diversity than degraded ones. Given these quite robust findings, how is it that diversity at a smaller scale is protective?

We use a family of statistical tools known as “meta-analysis” to aggregate the results of a number of previous studies into a single synthetic test of the dilution-effect hypothesis. It is well known that inferences drawn from small samples generally have lower precision (i.e., the estimates carry more uncertainty) than inferences drawn from larger samples. A nice demonstration of this comes from the classical asymptotic statistics. The expected value of a sample mean is the “true mean” of a normal distribution and the standard deviation of this distribution is given by the standard error, which is defined as the standard deviation of the distribution divided by the square root of the sample size. Say that for two studies we estimate the standard deviation of our estimate of the mean to be 10. In the first study, this estimate is based on a single observation, whereas in the second, it is based on a sample of 100 observations. The estimated of the mean in the second study is 10 times more precise than the estimate based on the first because while .

Meta-analysis allows us to pool estimates from a number of different studies to increase our sample size and, therefore, our precision. One of the primary goals of meta-analysis is to estimate the overall effect size and its corresponding uncertainty. The simplest way to think of effect size in our case is the difference in disease risk (e.g., as measured in the prevalence of infected hosts) between a species rich area and a species poor area. Unfortunately, a surprising number of studies don’t publish this seemingly basic result. For such studies, we have to calculate a surrogate of effect size based on the reported test statistics of the hypothesis that the authors report. This is not completely ideal — we would much rather calculate effect sizes directly, but to paraphrase a dubious source, you do a meta-analysis with the statistics that have been published, not with the statistics you wish had been published. On this note, one of our key recommendations is that disease ecologists do a better job reporting effect sizes to facilitate future meta-anlayses.

In addition to allowing us to estimate the mean effect size across studies and its associated uncertainty, another goal of meta-analysis is to test for the existence of publication bias. Stanford’s own John Ioannidishas written on the ubiquity of publication bias in medical research. The term “bias” has a general meaning that is not quite the same as the technical meaning. By “publication bias”, there is generally no implication of nefarious motives on the part of the authors. Rather, it typically arises through a process of selection at both the individual authors’ level and the institutional level of the journals to which authors submit their papers. An author, who is under pressure to be productive by her home institution and funding agencies, is not going to waste her time submitting a paper that she thinks has a low chance of being accepted. This means that there is a filter at the level of the author against publishing negative results. This is known as the “file-drawer effect”, referring to the hypothetical 19 studies with negative results that never make it out of the authors’ desk for every one paper publishing positive results. Of course, journals, editors, and reviewers prefer papers with results to those without as well. These very sensible responses to incentives in scientific publication unfortunately aggregate into systematic biases at the level of the broader literature in a field.

We use a couple methods for detecting publication bias. The first is a graphical device known as a funnel plot. We expect studies done on large samples to have estimates of the effect size that are close to the overall mean effect because estimates based on large samples have higher precision. On the other hand, smaller studies will have effect-size estimates that are more distributed because random error can have a bigger influence in small samples. If we plot the precision (e.g., measured by the standard error) against the effect size, we would expect to see an inverted triangle shape — or a funnel — to the scatter plot. Note — and this is important — that we expect the scatter around the mean effect size to be symmetrical. Random variation that causes effect-size estimates to deviate from the mean are just as likely to push the estimates above and below the mean. However, if there is a tendency to not publish studies that fail to support the hypothesis, we should see an asymmetry to our funnel. In particular, there should be a deficit of studies that have low power and effect-size estimates that are opposite of the hypothesis. This is exactly what we found. Only studies supporting the dilution-effect hypothesis are published when they have very small samples. Here is what our funnel plot looked like.

Note that there are no points in the lower right quadrant of the plot (where species richness and disease risk would be positively related).

While the graphical approach is great and provides an intuitive feel for what is happening, it is nice to have a more formal way of evaluating the effect of publication bias on our estimates of effect size. Note that if there is publication bias, we will over-estimate our precision because the studies that are missing are far away from the mean (and on the wrong side of it). The method we use to measure the impact of publication bias on our estimate of uncertainty formalizes this idea. Known as “trim-and-fill“, it uses an algorithm to find the most divergent asymmetric observations. These are removed and the precision of the mean effect size is calculated. This sub-sample is known as the “truncated” sample. Then a sample of missing values is imputed (i.e., simulated from the implied distribution) and added to the base sample. This is known as the “augmented” sample. The precision is then re-calculated. If there is no publication bias, these estimates should not be too different. In our sample, we find that estimates of precision differ quite a bit between the truncated and augmented samples. We estimate that between 4-7 studies are missing from the sample.

Most importantly, we find that the 95% confidence interval for our estimated mean effect size crosses zero. That is, while the mean effect size is slightly negative (suggesting that biodiversity is protective against disease risk), we can’t confidently say that it is actually different than zero. Essentially, our large sample suggests that there is no simple relationship between disease risk and biodiversity.

On Ecological Mechanisms One of the main conclusions of our paper is that we need to move beyond simple correlations between species richness and disease risk and focus instead on ecological mechanisms. I have no doubt that there are specific cases where the negative correlation between species richness and disease risk is real (note our title says that we think this link is idiosyncratic). However, I suspect where we see a significant negative correlation, what is really happening is that some specific ecological mechanism is being aliased by species richness. For example, a forest fragment with a more intact fauna is probably more likely to contain predators and these predators may be keeping the population of efficient reservoir species in check.

I don’t think that this is an especially controversial idea. In fact, some of the biggest advocates for the dilution effect hypothesis have done some seminal work advancing our understanding of the ecological mechanisms underlying biodiversity-disease risk relationships. Ostfeld and Holt (2004) note the importance of predators of rodents for regulating disease. They also make the very important point that not all predators are created equally when it comes to the suppression of disease. A hallmark of simple models of predation is the cycling of abundances of predators and prey. A specialist predator which induces boom-bust cycles in a disease reservoir probably is not optimal for infection control. Indeed, it may exacerbate disease risk if, for example, rodents become more aggressive and are more frequently infected in agonistic encounters with conspecifics during steep growth phases of their population cycle. This phenomenon has been cited in the risk of zoonotic transmission of Sin Nombre Virus in the American Southwest.

I have a lot more to write on this, so, in the interest of time, I will end this post now but with the expectation that I will write more in the near future!

This week in class I tried to take on the topic of complexity, as in “complex systems theory.” Complexity is a very important topic in human ecology, and biosocial science more generally. It’s also a topic that worries me a bit. It worries for two reasons. First, it seems all too easy for people to fall in with the cult of complexity and I believe that the weight of evidence shows very clearly that people are not at their best when they are associated with cults. If a perspective on science provides novel (especially testable!) insights, then I’m all for it. When it takes on the doctrinaire elements of a religion, then I’m less convinced of its value. The second reason complexity worries me is clearly related to the first. I am continually frustrated by anthropologists who, when confronted with complexity, throw their hands up and say it’s too complex to make predictions, why bother to do science or understand the principles underlying the system? You’d need to be trained as a theoretical physicist to understand the theory and people who think they understand something are just deluding themselves (or at least the rest of us) with a masculinist, hegemonic fantasy anyway. Let’s just tell a narrative (preferably peppered with some mind-numbing post-structuralist social theory). Better, perhaps, that we describe history. I think that this view is misguided to say the least (though I agree that history is fundamentally important).

There are three very influential reviews, all written for the Annual Review of Anthropology (when Bill Durham was editor, might I add), by eminent ecological anthropologists that have fed this perspective. Ian Scoones, Steve Lansing, and William Baleé each wrote a review between 1999 and 2006 more or less on the topic of complexity in human ecology. Scoones (1999) reviewed the ‘New Ecology’ and its implications for the social science. Lansing (2003) introduced complexity proper , and Baleé (2006) wrote about ‘Historical Ecology.’ I think its probably fair to say that each of these authors has a different sensibility regarding the role of science in anthropology.

Baleé advocates for the perspective of historical ecology, which emphasizes historical contingency and human agency in shaping landscapes. He seems to conflate systems ecology with an equilibrium episteme, noting that historical ecology is ‘at odds with systems ecology’ (Baleé 2006: 81) for the latter approach’s inability to allow human agency to increase biodiversity in some cases. This is an odd critique, since there is nothing inherent in any systems theory of ecological dynamics that makes this the case. He is also critical of island biogeography theory of MacArthur & Wilson (1967) because of its lack of attention to human agency as a cause of species invasions. Again, there is nothing inherent in island biogeography theory — or its modern inheritor, metapopulation biology — that excludes human agency as a mechanism for colonization. Presumably, the interested anthropologist could construct a model that included human facilitation of species invasions and explore both the transient and asymptotic (e.g., equilibrium) properties of this model.

Systems ecology, according to Baleé’s review, may have provided mathematical rigor to human ecology but it was static, ahistorical, and neglected political processes, a point first noted by Wolf in his Europe and the People without History. While it is certainly true that cultural ecologists studied relatively unstratified cultures (typically in isolation of other parts of the (human) world economic system), once again, there is nothing intrinsic in cultural ecology that makes this necessary. The idea of a cultural core (“the constellation of features which are most closely related to subsistence activities and economic arrangements” (Steward 1955:37)), central to Steward’s cultural ecology, is entirely applicable to stratified societies. It is more complex but that doesn’t make it irrelevant. Similarly, it seems that Steward’s multilinear evolutionary theory of culture, with its focus on broad cross-cultural patterns but emphasis of local particularities is also largely compatible with the tenets of historical ecology. I think that it is a fundamental misapprehension that every anthropologist who studies subsistence of face-to-face groups, following in the tradition of Julian Steward, is unaware of the larger political entanglements of foraging, farming, or pastoral people in a larger world political-economic system (see, e.g., Doug Bird‘s nice essay on the politics of Martu foraging). There is just a conditionality — or ‘bracketing’ if you prefer the phenomenological term — of subsistence activities. Given that the Martu or Hadza (or whoever) forage, how do they go about doing it? What are the consequences for landscapes in which they are embedded? These are legitimate, important, and interesting questions. So are questions about broader political economy. A little secret: They’re not mutually exclusive.

Lansing writes about complex systems proper, and about the phenomenon of emergence in particular. Emergence occurs when order arises solely out of local interactions and in the absence of central control. I agree completely with Lansing that an investigation of emergence is an important endeavor in ecological anthropology and, indeed, anthropology more generally. My concern that emerges from Lansing’s paper is simply the idea that we have no hope of understanding anything without really complex nonlinear models — models that are so complex they can only be instantiated in agent-based simulations. While I am engaged in the ideas of complex systems, I am not quite ready to give up on many traditional forms of analysis that use linear models. As we will see below, the devil is in the details in complex systems models and I don’t think it’s good for science to deprive ourselves of important suites of tools because of a priori assumptions about the nature of the systems we study. This statement should not be interpreted to mean that I think this is what Lansing is doing. I do worry about anthropologists who read this review being scared away from formal ecological analysis because the nonlinearity sounds scary.

It is Scoones (1999) who makes the most extreme statements about the consequences of complexity for human ecology. Regarding the three unifying themes around which the new human ecology was coalescing, he writes (1999: 490), “Third is the appreciation of complexity and uncertainty in social-ecological systems and, with this, the recognition of that prediction, management, and control are unlikely, if not impossible.” I think that this statement, while it may be an accurate description of some unifying themes in recent human ecology is simply incorrect and more than a bit nihilist. In all fairness, Scoones goes on to ask what the alternatives to the usual practice are (1999: 495):

So, what is the alternative to such a managerialist approach? A number of suggestions have been made. They generally converge around what has been termed “adaptive management” (Holling 1978, Walters 1976). This approach entails incremental responses to environmental issues, with close monitoring and iterative learning built into the process, such that thresholds and surprises can be responded to (Folke et al 1998).

This is a fair statement, which is rather at odds with the previous quote. If prediction and management are impossible, why is adaptive management a viable replacement? Does adaptive management not entail making predictions and, well, managing? Of course it does.

I have a series of critical questions that must be addressed before we accede to excessive complexity and stop trying to understand the process underlying human ecology.

With nonlinearity (as with stochasticity), the devil is in the details. What is the shape of the response? Sometimes nonlinear models are remarkably linear over the relevant parameter space and time scope. Sometimes they’re not. We don’t know unless we ask.

What is the strength of the response? With nonlinearity, the thing that matters for the difficulty in prediction, sensitivity to initial conditions, etc. is the strength of response. Sometimes this strength is not that high and linear models work amazingly well.

How big are the possible perturbations? We might be able to make quite good predictions if perturbations are small. Of course, we shouldn’t assume that perturbations are always small (as much classical analysis does). This is an empirical question.

What is the effect of random noise? Some of the deterministic models with exotic dynamics collapse into pretty standard models in the presence of noise. Of course, sometimes randomness makes prediction even harder — this is partly a function of the previous three points (i.e., the shape of nonlinearity, the strength of the response, and the size of perturbations).

A couple figures can illustrate two of these points. Consider the following hypothetical recruitment plot. On the x-axis, I have plotted the population size, while on the y-axis, I have plotted the number of recruits born. Suppose that the actual underlying process for recruitment was density-dependent (i.e., was nonlinear), as indicated by the dashed line. In this particular hypothetical case, you would not do all that badly with a linear model (solid line). As we move across three orders of magnitude, the difference in recruitment between the linear and nonlinear models is two births. The process of recruitment is nonlinear (i.e., it’s density-dependent) but you would do just fine with predictions based on a linear model.

Taking up on Bob May’s classic (1976) paper, we can use the logistic map (a discrete-time logistic population growth model) to look at strength of response. The logistic map is given by the following nonlinear difference equation . We can plot the relationship between and . This shows the classic symmetric, humped recruitment curve characteristic of the logistic model. Where a line intersects the recruitment curve, the model has a fixed point. The stability of these fixed points is determined by the slope of the tangent line at the intersection of the curves. If the absolute value of this slope is greater than one, perturbations from the fixed point will grow — the model is unstable. If the absolute value of this slope is less than one, then any trajectory in the neighborhood will return to the fixed point. The parameters used to make these figures create a complex 2-point series (i.e., the population oscillates between two fixed points) on the left-hand case, while for the right-hand case, there is a simple fixed point. By cranking up the parameter in the logistic map, we can induce more and more exotic dynamics. However, the key point here is that if the response is weak enough, the dynamics are not especially exotic at all. Note that we start to get the interesting behavior at values of , or a tripling of population size each time step. Human populations do not grow nearly this fast. Not even close. This isn’t to say that some human processes with nonlinear dynamics don’t have very strong responses, but clearly not all must. Population growth is a pretty important problem for human ecology, and it’s dynamics are unlikely to be really exotic. Maybe we can use some simple models to understand human population dynamics? See last week’s post on the work of Tuljapurkar and colleagues for some exemplary contemporary work.

So, there are two cases where understanding the nature of the nonlinearity makes an enormous difference in how we make predictions and otherwise understand the system. Sometimes nonlinear models are effectively linear over important ranges of parameter space. Sometimes the response of a nonlinear model is small enough that the system shows very predictable, well-mannered dynamics. But just so you don’t think that I don’t think complexity is an issue, let’s look at one more example. This model is from a classic study by Hastings and Powell (1991) showing chaos in a simple model of a food chain.

The model has three species: producer, primary consumer, secondary consumer; and it is a simple chain (secondary consumer eats primary consumer eats producer). Hastings and Powell chose the model parameters to be biologically realistic — there’s nothing inherently wacky about the way the model is set up. Using the same parameters that they use to produce their figure 2, I numerically solved their equations (using deSolve in R). The first plot shows the dynamics in time, with the bizarre oscillations in all three species.

In the second figure, I reproduce (more or less) their three-dimensional phase plot, which takes time out of the plot and instead plots the three population series directly against each other.

Finally, I plot some pair-wise phase-plots, which are easier to visualize than the false 3D image above.

On the whole, we see very complex behavior in a rather simple food chain. Hastings and Powell (1991: 901-902) summarize their findings: (1) contrary to conventional wisdom, they suggest that chaos need not be rare in nature, (2) chaotic behavior “need not lead to an erratic and unpatterned trajectory in time that one might infer from the usual (not mathematical) connotation of the word ‘chaos'” and (3) time scales matter tremendously — over short time scales, the behavior of the system is quite regular.

For me, the greatest lesson from the complex systems approach is the need to understand the specific details. Contrary to the inclination to throw up one’s hands at the thought of a science of human ecology (let alone putting this science into practice with sensible management policies), it seems that the issues raised here mean that we should study these systems more, attempting to understand both their historical trajectories and the principles upon which they are organized. By all means, let’s jettison old-fashioned ideas about typology and homeostasis in nature. No need to keep around the clockworks metaphor of ecological succession or the idea that the Dobe !Kung are Pleistocene remnants. Ecosystems, landscapes, whatever term you want to use, don’t necessarily tend toward equilibria. Uncertainty is ubiquitous. People are part of these systems and have been for a long time. Good, we’re agreed. But can we please not give up on using all the scientific tools we have at our disposal to understand these complex systems in which human beings are embedded? Anthropologists have much to contribute to this area, not the least of which is long-term, place-based research on human-environmental systems.

The lesson of prediction over the short-term is another issue that comes up repeatedly in the complex systems literature. I think that the work of George Sugihara and colleagues is especially good on this front. I have blogged (here and here) about a paper on which he is a co-author before (I should note that in this paper they suggest ways to make predictions of catastrophic events in complex systems with noise — just sayin’). There is a nice, readable article in Scientific American on his work on fisheries that summarizes the issues. This work combines so many things that I like (demography, fish, statistics, theoretical ecology, California), it’s a bit scary. Another nice, readable piece that also describes some of Sugihara’s work in finance can be found in SEED magazine here.

This post is already too long. I clearly will need to write about the other topic for the week, risk and uncertainty, at a later date.

References

Baleé, W. 2006. The research program of historical ecology. Annual Review of Anthropology. 35:75-98.

Our new paper at PNAS has been out a day now and Wired Magazine has already done a story on it. It’s a nice piece but it gets several things hilariously wrong. It says:

Bird’s team recently published a study on “fire stick farming,” a traditional method of ecosystem management still used by aborigines in Australia’s Western Desert. By burning wooded areas, lizards are driven towards hunters; cookpot-friendly kangaroos and emus fatten themselves on grasses flourishing on newly cleared lands.

The thing is that (1) Martu don’t use fire to drive game, and (2) Murtu don’t burn woodland — only spinifex grassland. That’s really what drives the process. Spinifex may be bullet-proof. It may puncture the tires on your Land Rover. It may eat other plant species for breakfast. But, boy, does it burn! By burning spinifex, Martu hunters open the grasslands up for colonization by early successional species that couldn’t otherwise compete. From a hunter’s perspective, burning increases access to goanna burrows and therefore increases foraging returns.

Science reporting is hard. You have to turn around comprehensible — and compelling — stories on tight deadlines. It’s nonetheless a shame that this piece gets such a fundamental piece of the story wrong. One thing that is very nice, however, is that there is a link to the actual paper.

According to Hanna Kokko’s Anti-Finglish kit for ecologists, the Finnish term for the basic reproduction number is lisääntymiskerroin. That’s awesome. I think that’s what I’m going to call it from now on … if I can just figure out how to pronounce that!